Respiration rate has proven to be a good indicator of the deterioration of the condition of a patient and it plays a crucial role in early warning hospital systems in combination with other vital body signs. Therefore, a need for continuous and reliable monitoring of a respiration signal is seen especially in the intensive care units of hospitals. A similar need, with less stringent requirements on the instantaneous presentation of the monitored parameters, is present in the general ward settings of hospitals and in home healthcare applications, such as in telemedicine and chronic disease management. While continuous monitoring of the respiration signal, from which the respiration rate is extracted, is available on bedside monitors for intensive care patients, various portable sensor systems are being developed to allow unobtrusive and prolonged measurement and monitoring of the respiration signal of mobile patients in general wards with minimal discomfort.
Respiratory monitoring can be based on different principles: the measurement of respiratory effort, for example thorax impedance plethysmography, accelerometers, photoplethysmography, or the measurement of respiratory effect, for example sound recording, temperature sensing, carbon dioxide sensing. Some sensors are already well established to monitor respiration in applications other than general ward. In intensive care units for example, thorax impedance-plethysmography is the method of choice, whereas in sleep studies inductive plethysmography, often referred to as respiration band, is also commonly used. In ambulatory patients, such as on the general ward or in home healthcare, these sensors have limitations. A respiration band, for example, is considered to be too obtrusive by both medical personnel and patients.
A respiration monitoring system based on a multi-axial accelerometer overcomes this disadvantage. A multi-axial accelerometer is a device that measures the acceleration in multiple sensing axes, and is used as an inclinometer to reflect the abdomen or chest movement caused by respiration. This technique requires reliable signal processing methods to enable reliable monitoring under different conditions and postures of the patient.
Motion artifact is a well known issue in patient monitoring as a whole, which refers to the contamination of the physiological signal and the degradation of the measurement quality caused by physical activities of a patient, such as posture change, movement and talking. The motion artifact issue is more pronounced in a general ward setting than in an intensive care unit setting, since patients in the general ward setting generally have a more mobile activity pattern and are monitored most of the time without supervision of hospital staff, thus lacking knowledge on the presence of physical activities. The problem becomes even more severe in the monitoring of patients in home healthcare settings.
If a multi-axial accelerometer is used to measure respiration rate in ambulatory conditions such as home healthcare or patients on a general ward, the accelerometer signals do not only change due to the respiration of a person but the accelerometer signals are also affected by unwanted motions, that are not caused by respiratory motions, such as whole-body movements, such as for example walking or running, and other physiological motions, such as for example due to heart beat. Some of these unwanted motions, which may have frequency components in the same range of the respiration, i.e. 0.1 Hz to 2 Hz or 6 respirations per minute to 120 respirations per minute, cannot be suppressed with a filter with a fixed frequency response.
U.S. Pat. No. 6,997,882 B1 discloses a method and device for processing accelerometer data to derive information about the respiratory movements of a subject. The method applies an array of four uni-axial accelerometer modules worn on the pelvis of a subject and separates the acceleration of the anterior aspect of the pelvis from the posterior aspect of the pelvis. The fundamental premise of this approach is that respirations have a disproportionate effect on the anterior aspect of pelvic motion, which can be exploited using a differential technique. In particular, the isolation of a high signal-to-noise ratio respiratory signal is accomplished using an adaptive noise-cancellation algorithm that employs the least means square filtering technique. The approach treats the net acceleration in the summed (horizontal plane) anterior accelerometer channels as representing the signal of interest, i.e. acceleration due to respirations, plus noise, whereas the summed (horizontal plane) posterior accelerometer signal represents mainly noise, which is, however, highly correlated with the noise in the composite anterior accelerometer signal. The noise is due mainly to accelerations caused by motion of the pelvis in the transverse plane, such as during sway, walking, and running. A disadvantage of this method is that it requires an array of accelerometer modules which have to be worn by a subject.